no code implementations • 5 Feb 2024 • Yixiang Shan, Zhengbang Zhu, Ting Long, Qifan Liang, Yi Chang, Weinan Zhang, Liang Yin
The performance of offline reinforcement learning (RL) is sensitive to the proportion of high-return trajectories in the offline dataset.
no code implementations • 4 Feb 2024 • Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, Weinan Zhang
In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets.
no code implementations • 8 Oct 2022 • Yixiang Shan, Jielong Yang, Xing Liu, Yixing Gao, Hechang Chen, Shuzhi Sam Ge
Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations.